Lin-Lin Wang
Papers
1
Total Citations
9
H-Index
1
About
Lin-Lin Wang is a robotics researcher whose work focuses on enhancing autonomous navigation systems for critical applications, particularly in hazardous environments. Wang’s primary research areas include path planning, model predictive control, and rescue robotics, with a strong emphasis on improving safety and efficiency in coal mine rescue operations. Their most cited work, "Research on SBMPC Algorithm for Path Planning of Rescue and Detection Robot" (2020, 9 citations), introduces a novel Sampling-Based Model Predictive Control (SBMPC) algorithm that integrates predictive control principles into autonomous path planning. This contribution addresses the urgent need to eliminate human risk in dangerous rescue scenarios by enabling robots to navigate complex, unpredictable underground environments more reliably. Wang’s research directly enhances the security and effectiveness of rescue missions, demonstrating a clear impact on both robotics theory and practical disaster response. With a growing citation record, Wang is recognized for bridging algorithmic innovation with real-world safety challenges, making their work a valuable reference for students and researchers interested in autonomous systems, control theory, and humanitarian robotics.
Research Focus
Key Achievements
Top Papers
- 1